Applications of bionformatics in Drug Disovery

The challenge for bioinformatics is to create short lists of targets most likely to be ‘druggable’ from long lists of genes. Bioinformatics can help with three aspects of target selection:
1. classification of protein families;
2. an understanding of the biochemical and cellular behaviour of the targets;
3. the development of targets involving predictions on detoxification, stratification of patient populations and other gene-based variations.
The term ‘in silico biology’ is used for conducting biology before setting foot in the laboratory for traditional ‘wet’ biology. Bioinformatics has made this possible but it should not be misunderstood as a replacement for ‘wet’ biology. Using simple point-and-click commands, researchers can quickly perform a variety of biological analyses in silico.
The detection of similarities between amino acid sequences is fundamental to pharmaceutical research in silico. Conventional techniques can only detect relationships between sequences when at least 25% of their residues match.
 Combination of In Silico and In vitro Studies
Information obtained in silico combined with information provided by bacteria growing on Petri dishes may lead to novel drug targets and new insights into the nature of conserved and essential genes in organisms. It is recommended that, apart from prediction of the gene function, it is worthwhile to get as much information out of the computer whenever drug design is potentially involved. This narrows the range of necessary experiments and saves time and money.
Metabolic instability is a common limiting factor in obtaining acceptable ADME early in the drug discovery process. Metabolite identification is helpful but early access to information is limited. A high metabolic clearance may raise the question of where the drug is being metabolised. Early identification of the metabolite and structure–metabolism relationship may facilitate early discovery decisions.
Bioinformatic support in this situation is valuable. The increasing volume and complexity of biological and chemical data require the use of bioinformatic systems to extract knowledge relevant to decision making for the drug discovery process. The largest and most important category of information is genomic and proteomic databases with further studies in gene/protein and disease associations and population genetics. Databases can now communicate with each other over the Internet. The daunting task of navigating through the massive amount of data requires simple and integrated tools to generate innovative but meaningful information for drug discovery. The bioinformatic tools should be able to cope with more than 100 drug discovery approaches that are now available.